AUC Optimization Based Confidence Measure for Keyword Spotting

Confidence measure plays an important role in keyword spotting. To enhance the effectiveness of the confidence measure, we propose a novel method which improves the performance of keyword spotting by directly maximizing the area under the ROC curve (AUC). Firstly, we approximate the AUC as an objective function with the weighted mean confidence measure. Then, we optimize the objective function by training the weighting factors with the generalized probabilistic descent algorithm. Compared with the current method based on minimum classification error (MCE) criterion, the proposed method makes a global enhancement of ROC curve and does not need to train any threshold. The experiments conducted on the King-ASR023 database show that the proposed method outperforms both the method averaging phone-level confidences and the method based on MCE.